About

In September 2020, I joined Google full time as a Data Scientist.

I recently submitted my PhD thesis and am waiting for my PhD viva. As a PhD student, I spent 3+ years at the MRC Biostatistics Unit of the University of Cambridge, where I was part of the Statistical Genomics (SOMX) and Precision Medicine (PREM) research groups. My PhD supervisor is Dr Paul Kirk.

During my PhD, I also spent three months working at The Alan Turing Institute, under the supervision of Dr Anthony Lee and Dr Ioannis Kosmidis, and six months at Google, hosted by Dr Matthew Pearce and Dr Georg Goerg.

Prior to starting my PhD, I completed a double degree in Engineering Mathematics at Politecnico di Milano and École Centrale de Nantes. I also had the chance to do my Master’s thesis at the Statistical Laboratory of the University of Cambridge, co-supervised by Dr Davide Pigoli and Prof Piercesare Secchi, and my undergraduate thesis at INRIA, where I worked with Dr Paola Goatin.

Research

My PhD research is concerned with the development of statistical methodology for the integration of multiple ‘omics datasets (e.g. genomic, transcriptomic, proteomic, etc.) in personalised medicine.

My goal is to tackle some of the challenges presented by the identification of relevant patient subgroups (e.g. patients that might be expected to respond similarly to treatments) on the basis of those datasets.

First, when combining different types of ‘omics datasets, it is crucial to take into account the different nature of each dataset. For this reason, I am developing integrative clustering methods that explicitly weigh the contribution of each dataset to the final clustering according to the amount of information that it contains, and that allow to combine datasets of different type (e.g. continuous, categorical, etc.). These methods are based on the idea that the output of classical statistical techniques such as model-based Bayesian clustering can be used in combination with kernel methods from the machine learning literature to find a meaningful global clustering that summarises all the information available.

Second, because genomics datasets comprise measurements taken on a very large number of variables, many different patient subgroups can usually be identified, depending on which variables we include in our analysis. For this reason, I am also working on integrating genetic information with data on specific patient outcomes, to ensure that we identify truly relevant patient subgroups. To do so, I have generalised the method above to the supervised case, and I am now going to implement a variational inference algorithm for outcome-guided model-based Bayesian clustering, as an alternative to that.

On a more applied note, I am participating in a study on cardiovascular disease. My role in the project is to analyse data collected at the Cambridge Blood Donor Centre with the statistical methods mentioned above, to define a personalised cardiovascular disease risk score.

References:

Previous research
High performance, large scale regression

During my internship at The Alan Turing Institute, I explored different methods and libraries to perform high-performance, large-scale regression on a supercomputer, with particular focus on Apache Spark and TensorFlow. The internship was funded by Cray Inc and carried out in close collaboration with the Cray EMEA Research Lab. You can find more details about our findings on the blog and the official webpage of the project.

Permutation tests for functional and network data
Macroscopic traffic flow models
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Team OPALE (now ACUMES), INRIA — Sophia Antipolis, France — Summer 2013.

Presentations

I will soon be at:

I have recently presented my work at:

  • Google’s PhD Intern Research Conference, Sunnyvale, USA — 31 July 2019 — Poster
  • Statistical Omics discussion group, Cambridge, UK — 24 April 2019 [slides]
  • ERCIM CMStatistics, Pisa, Italy — 14-16 December 2018 — Invited talk
  • Armitage week, Cambridge, UK — 14-15 November 2018 — PhD talk
  • The Alan Turing Institute, London, UK — 10 September 2018 — Joint talk with Junyang Wang [slides, video]
  • Start Up Research, Palermo, Italy — 19 June 2018 — Joint talk with Alessandro Casa [slides]
  • ISNPS, Salerno, Italy — 11-15 June 2018 — Invited talk [slides]
  • University of Oslo, Norway — 1-2 March 2018 — Visiting
  • SMPGD, Montpellier, France — 11-12 January 2018 — Contributed talk
  • Biometrika workshop, Cambridge, UK — 10 November 2017 — Contributed talk
  • SMPGD, London, UK — 12-13 January 2017 — Poster
Laurea
Master’s thesis defence — Milan, Italy — July 2016.
Photo by Paolo Cabassi.

Events

Past events

Start Up Research was a collaborative project organised by y-SIS on Statistics for the Neurosciences that brought together 28 other young academics and seven professors from some of the most prestigious universities worldwide. The resulting research was subsequently published in a Springer volume entitled “Studies in Neural Data Science”. To learn more about the event, have a look at this article on the statistics magazine Significance.

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Start Up Research — Certosa di Pontignano, Italy — 25-27 June 2017.
From right to left: Alessandro Casa, Massimiliano Russo, me, and Matteo Fontana.

Stats Under the Stars is a statistical hackaton organised by the Italian Statistical Society. I took part in the competition with the Celtic team in 2016, when we won the prize for the best report, and in 2017.

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Celtic team, SUS2 — Vietri sul Mare, Italy — 7-8 June 2016.
From right to left: Anna Calissano, Giorgio Paulon, Tobia Boschi, Jacopo Di Iorio, and me.

More recently, I was selected for the Women As Tech Leaders Day at QuantumBlack, that took place on 16 November 2018 in their London office.

Societies

Mentoring

I am a mentor at Lead The Future, a nonprofit providing 1:1 mentoring to high-potential Italian students, since 2019. This year, I am mentoring 5+ students at top European and American universities and research institutes. Forbes talked about Lead The Future in this article (in Italian).

I also mentor one student a year through Central Nantes Alumni and four students via A.I.M. (details below).

Scientific societies

A.I.M.

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Illustration by Marta Muscelli for A.I.M.

A.I.M. (Associazione Ingegneri Matematici) is a student society with the goal of promoting the concept of Engineering Mathematics and creating a network between students and alumni from Politecnico di Milano, to facilitate the exchange of ideas, opinions and advice about the academic and professional life. Find out more about what we do with this amazing presentation!

I was the president of the committee in 2014 and continued contributing to the Association ever since.

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A.I.M. Online party — Milan, Italy — May 2014.
Photo by Giorgio Paulon.